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Intelligent technology-based control of motion and vibration using MR dampers

 Zhou  Li (周丽)1, Chih-Chen Chang (张志成)2 and  B. F. Spencer, Jr. (苏磐石)3

  1. College of Aerospace Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China
  2. Department of Civil Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
  3. Department of Civil & Environmental Engineering, University of Illinois at Urbana-Champaign, 205 North Matthews Ave, Urbana, IL 61801, USA

Abstract: Due to their intrinsically nonlinear characteristics, development of control strategies that are implementable and can fully utilize the capabilities of semiactive control devices is an important and challenging task. In this study, two control strategies are proposed for protecting buildings against dynamic hazards, such as severe earthquakes and strong winds, using one of the most promising semiactive control devices, the magnetorheological (MR) damper. The first control strategy is implemented by introducing an inverse neural network (NN) model of the MR damper. These NN models provide direct estimation of the voltage that is required to produce a target control force calculated from some optimal control algorithms. The major objective of this research is to provide an effective means for implementation of the MR damper with existing control algorithms. The second control strategy involves the design of a fuzzy controller and an adaptation law. The control objective is to minimize the difference between some desirable responses and the response of the combined system by adaptively adjusting the MR damper. The use of the adaptation law eliminates the need to acquire characteristics of the combined system in advance. Because the control strategy based on the combination of the fuzzy controller and the adaptation law doesn’t require a prior knowledge of the combined building-damper system, this approach provides a robust control strategy that can be used to protect nonlinear or uncertain structures subjected to random loads.

Keywords: neural networks; models; fuzzy control; adaptation law; nonlinear structure; MR dampers

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Copyright© 2009 IEM. Journal of Earthquake Engineering and Engineering Vibration. All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as described below, without written permission from the Publisher. Copying of articles is not permitted except for personal and internal use, to the extent permitted by national copyright law, or under the terms of a license issued by the National Reproduction Rights Organization of China.